Why Do Multiple Unnamed Python Objects Sometimes Share the Same ID?

Object Identity in Python: Why Multiple Unnamed Objects Share the Same ID

When creating multiple objects of a given class in Python, one might expect each instance to possess a unique identity (ID). However, under certain circumstances, successive creations of unnamed objects may result in them having the same ID.

Understanding Object Lifetime and ID

The ID of an object represents its unique location in memory. However, this ID is only guaranteed to be unique during the object's lifetime, not throughout the program's execution.

In the example provided, you are creating two unnamed instances of the someClass class using print:

print(someClass())
print(someClass())

Once print returns, the created objects are no longer referenced by any variable and are eligible for garbage collection. In Python's CPython implementation, they are effectively deallocated immediately.

CPython Implementation Details

Python's garbage collection occurs through reference counting. Additionally, the ID of an object is related to its memory location. When the first unnamed object is deallocated, the memory location it occupied becomes available.

Consequently, the next object created is placed in the same memory location, resulting in the same ID being assigned to both objects.

Solution

To ensure distinct IDs for objects, consider keeping references to them in a list or implementing a class-specific ID that provides appropriate guarantees, such as:

class SomeClass:
    next_id = 0

    def __init__(self):
         self.id = SomeClass.nextid
         SomeClass.nextid += 1

By implementing your own ID generator, you can create unique IDs for each object regardless of their lifetime.

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